Remote AI Data Trainer: Hardware & Systems

Alignerr Corp.; Wollongong City Council; Remoto; AUD 70.000 - 120.000; Tempo pieno

Vantaggi offerti da questo lavoro

Autonomy

Flexible schedule

Global collaboration

Descrizione del lavoro

Alignerr invites applications for a remote, hourly Computer Engineering - AI Data Trainer. This freelance role offers 10–40 hours per week with flexible remote collaboration across time zones.

Your work includes designing advanced challenges in RISC‑V/ARM, creating golden solutions with HDL snippets and diagrams, auditing AI-generated code and kernels, and giving structured feedback to improve model reasoning.

Competenze

• Masters or PhD in Computer Engineering, Computer Science with hardware focus, or closely related field.
• Strong foundational knowledge in core areas such as Computer Architecture, Embedded Systems, Digital Logic Design, or Operating Systems.
• Analytical writing to convey highly technical hardware concepts and low-level software logic clearly and concisely.

Mansioni

• Design advanced computer engineering challenges across domains like RISC‑V/ARM, FPGA development, memory management, and hardware‑software co‑design.
• Author ground‑truth solutions with step‑by‑step technical details, including HDL snippets and architectural diagrams.
• Perform technical auditing of AI‑generated code (C/C++, Verilog, VHDL), logic gates, and OS kernels for accuracy and standards.
• Refine AI reasoning by identifying issues like race conditions and memory leaks and provide structured feedback.

Conoscenze

Analytical writing

Attention to detail

Computer Architecture

Embedded Systems

Digital Logic Design

Operating Systems

Formazione

Master's degree

PhD

Strumenti

SolidWorks

MATLAB

ANSYS

Descrizione del lavoro

Alignerr invites applications for a remote, hourly Computer Engineering - AI Data Trainer. This freelance role offers 10–40 hours per week with flexible remote collaboration across time zones.

Your work includes designing advanced challenges in RISC‑V/ARM, creating golden solutions with HDL snippets and diagrams, auditing AI-generated code and kernels, and giving structured feedback to improve model reasoning.